Efficiency and Reliability Joint Optimization of Chiller Plants Based on a Hybrid Model

Zhang, Danxu; Mittal, Khushboo; Wilson, James P.; Luh, Peter B.; Fan, Junqiang; Gupta, Savita

doi:10.1109/lra.2019.2924126

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…where c m is the cost of material per workpiece. Thus, the expected material cost is proportional to the expected number of attempts until success, as shown in Equation (19).…”

Section: Expected Materials Costmentioning

confidence: 99%

“…On the other hand, Han et al [ 18 ] optimizes for energy efficiency under fault‐free situations while ensuring there is enough reserve capacity in the presence of mild faults. Several other issues, such as tool degradation, long term reliability, [ 19 ] varying quality control, scrap production, and delay are generally not considered.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Wilson

Shen

Awasthi

et al. 2022

J Adv Manuf & Process

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With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost-efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi-objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi-objective formulation. The optimization framework is tested on a high-fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to 15% as compared to the default control scheme.

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“…where c m is the cost of material per workpiece. Thus, the expected material cost is proportional to the expected number of attempts until success, as shown in Equation (19).…”

Section: Expected Materials Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Wilson

Shen

Awasthi

et al. 2022

J Adv Manuf & Process

Self Cite

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“…(Please, provide the manuscript number! )Weichhart (2017)), plant factory crop scheduling(Huang et al (2020)), unit commitment(Kim et al (2018),Chen et al (2019),Li et al (2019),Chen et al (2020),Li et al (2020)), neural network acceleration(Peng et al (2021)), semiconductor manufacturing(Chang and Dong (2017)), chiller-plant optimization(Zhang et al (2019)), slot allocation of a liner container shipping service), and transmission and distribution coordination(Bragin and Dvorkin (2021)).…”

mentioning

confidence: 99%

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming

Bragin¹

2022

Preprint

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Mixed-Integer Linear Programming problems, playing a prominent role in Operations Research, are prone to the curse of dimensionality; specifically, the combinatorial complexity of the associated MILP problems increases exponentially with the increase of the problem size. To efficiently solve the "separable" MILP combinatorial problems, decomposition, and coordination Surrogate "Level-Based" Lagrangian Relaxation method is developed. The new method efficiently exploits the underlying geometric-convergence potential -the best convergence theoretically possible -inherent to Polyak's step-sizing formula in the dual space without the need to know the optimal dual value required for convergence. Unlike all the previous methods to obtain "level" estimates of the optimal dual value by adjusting hyperparameters, the key novel idea to obtain "level" value is through a hyper-parameter-free optimization: a novel "multiplier-convergence-feasibility" Linear Programming constraint satisfaction problem needs to be solved, from which, in conjunction with the Polyak's formula, the sought-for "level" estimate is inferred. Testing results for medium-and largescale instances of standard Generalized Assignment Problems from OR-library demonstrate that 1. "level" estimates generally decrease and the dual values increase faster than those obtained by using other existing methods, 2. the new method is computationally efficient in terms of the quality of solutions (for several large-scale cases, optimal solutions are obtained) and in terms of the CPU time, 3. the new method is robust with respect to the choice of initial parameters such as step-sizes and multipliers. Moreover, the procedure to estimate the "level" value is independent of linearity and separability of the problem, and thus the new method also paves the way for efficient resolution of general Mixed-Integer Programming (MIP) problems.

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Non-Linear Autoregressive with Exogenous Input (Narx) Chiller Plant Prediction Model

Zabidi

Jaya

Din

et al. 2021

2021 International Conference on Software Engineering &Amp; Computer Systems and 4th International Conference on Computational

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Efficiency and Reliability Joint Optimization of Chiller Plants Based on a Hybrid Model

Cited by 3 publications

References 19 publications

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Multi‐objective optimization for cost‐efficient and resilient machining under tool wear

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming

Non-Linear Autoregressive with Exogenous Input (Narx) Chiller Plant Prediction Model

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